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This research article presents a machine learning framework designed to predict the stability and sustainability of next-generation battery materials, utilizing data from the Materials Project. The study demonstrates that the Random Forest model outperforms XGBoost in predicting formation energy and sustainability indices, achieving high reliability and predictive accuracy. The findings highlight the potential of machine learning to accelerate the discovery of environmentally friendly materials for energy s

Author

godfrey.oise

Language

EN